Aryeh Kontorovich

According to our database, Aryeh Kontorovich authored at least 96 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Nested barycentric coordinate system as an explicit feature map for polyhedra approximation and learning tasks.

Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele

Mach. Learn., October, 2024

Correction: Efficient Kirszbraun extension with applications to regression.

Hananel Zaichyk, Armin Biess, Aryeh Kontorovich, Yury Makarychev

Math. Program., September, 2024

Efficient Kirszbraun extension with applications to regression.

Hananel Zaichyk, Armin Biess, Aryeh Kontorovich, Yury Makarychev

Math. Program., September, 2024

Fat-Shattering Dimension of k-fold Aggregations.

Idan Attias, Aryeh Kontorovich

J. Mach. Learn. Res., 2024

Functions with average smoothness: structure, algorithms, and learning.

Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich

J. Mach. Learn. Res., 2024

Aggregation of expert advice, revisited.

Aryeh Kontorovich

CoRR, 2024

Distribution Estimation under the Infinity Norm.

Aryeh Kontorovich, Amichai Painsky

CoRR, 2024

Splitting the Difference on Adversarial Training.

Matan Levi, Aryeh Kontorovich

Proceedings of the 33rd USENIX Security Symposium, 2024

Agnostic Sample Compression Schemes for Regression.

Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Correlated Binomial Process.

Moïse Blanchard, Doron Cohen, Aryeh Kontorovich

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Efficient Agnostic Learning with Average Smoothness.

Steve Hanneke, Aryeh Kontorovich, Guy Kornowski

Proceedings of the International Conference on Algorithmic Learning Theory, 2024

2023

Dimension-Free Empirical Entropy Estimation.

Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer

IEEE Trans. Inf. Theory, May, 2023

Tree Density Estimation.

László Györfi, Aryeh Kontorovich, Roi Weiss

IEEE Trans. Inf. Theory, February, 2023

Near-optimal learning with average Hölder smoothness.

Guy Kornowski, Steve Hanneke, Aryeh Kontorovich

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Open problem: log(n) factor in "Local Glivenko-Cantelli.

Doron Cohen, Aryeh Kontorovich

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Local Glivenko-Cantelli.

Doron Cohen, Aryeh Kontorovich

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Domain Invariant Adversarial Learning.

Matan Levi, Idan Attias, Aryeh Kontorovich

Trans. Mach. Learn. Res., 2022

Learning Convex Polyhedra With Margin.

Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch

IEEE Trans. Inf. Theory, 2022

Improved Generalization Bounds for Adversarially Robust Learning.

Idan Attias, Aryeh Kontorovich, Yishay Mansour

J. Mach. Learn. Res., 2022

Non-uniform packings.

Lee-Ad Gottlieb, Aryeh Kontorovich

Inf. Process. Lett., 2022

Differentially-Private Bayes Consistency.

Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer

CoRR, 2022

Metric-valued regression.

Dan Tsir Cohen, Aryeh Kontorovich

CoRR, 2022

Adaptive Data Analysis with Correlated Observations.

Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer

Proceedings of the International Conference on Machine Learning, 2022

Learning with metric losses.

Dan Tsir Cohen, Aryeh Kontorovich

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Fat-shattering dimension of k-fold maxima.

Aryeh Kontorovich, Idan Attias

CoRR, 2021

Apportioned margin approach for cost sensitive large margin classifiers.

Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich

Ann. Math. Artif. Intell., 2021

Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound.

Steve Hanneke, Aryeh Kontorovich

Proceedings of the Algorithmic Learning Theory, 2021

Nested Barycentric Coordinate System as an Explicit Feature Map.

Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Non-parametric Binary regression in metric spaces with KL loss.

Ariel Avital, Klim Efremenko, Aryeh Kontorovich, David Toplin, Bo Waggoner

CoRR, 2020

On biased random walks, corrupted intervals, and learning under adversarial design.

Daniel Berend, Aryeh Kontorovich, Lev Reyzin, Thomas J. Robinson

Ann. Math. Artif. Intell., 2020

Learning discrete distributions with infinite support.

Doron Cohen, Aryeh Kontorovich, Geoffrey Wolfer

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Universal Bayes Consistency in Metric Spaces.

Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss

Proceedings of the Information Theory and Applications Workshop, 2020

Algorithmic Learning Theory 2020: Preface.

Aryeh Kontorovich, Gergely Neu

Proceedings of the Algorithmic Learning Theory, 2020

Minimax Testing of Identity to a Reference Ergodic Markov Chain.

Geoffrey Wolfer, Aryeh Kontorovich

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Fast and Bayes-consistent nearest neighbors.

Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Optimality of SVM: Novel proofs and tighter bounds.

Steve Hanneke, Aryeh Kontorovich

Theor. Comput. Sci., 2019

Regression via Kirszbraun Extension with Applications to Imitation Learning.

Armin Biess, Aryeh Kontorovich, Yury Makarychev, Hanan Zaichyk

CoRR, 2019

Estimating the Mixing Time of Ergodic Markov Chains.

Geoffrey Wolfer, Aryeh Kontorovich

Proceedings of the Conference on Learning Theory, 2019

Minimax Learning of Ergodic Markov Chains.

Geoffrey Wolfer, Aryeh Kontorovich

Proceedings of the Algorithmic Learning Theory, 2019

Sample Compression for Real-Valued Learners.

Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi

Proceedings of the Algorithmic Learning Theory, 2019

A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes.

Steve Hanneke, Aryeh Kontorovich

Proceedings of the Algorithmic Learning Theory, 2019

Improved Generalization Bounds for Robust Learning.

Idan Attias, Aryeh Kontorovich, Yishay Mansour

Proceedings of the Algorithmic Learning Theory, 2019

Temporal Anomaly Detection: Calibrating the Surprise.

Eyal Gutflaish, Aryeh Kontorovich, Sivan Sabato, Ofer Biller, Oded Sofer

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Near-Optimal Sample Compression for Nearest Neighbors.

Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

IEEE Trans. Inf. Theory, 2018

Agnostic Sample Compression for Linear Regression.

Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi

CoRR, 2018

A New Lower Bound for Agnostic Learning with Sample Compression Schemes.

Steve Hanneke, Aryeh Kontorovich

CoRR, 2018

Advanced Analytics for Connected Car Cybersecurity.

Matan Levi, Yair Allouche, Aryeh Kontorovich

Proceedings of the 87th IEEE Vehicular Technology Conference, 2018

Learning convex polytopes with margin.

Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Efficient Regression in Metric Spaces via Approximate Lipschitz Extension.

Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer

IEEE Trans. Inf. Theory, 2017

Active Nearest-Neighbor Learning in Metric Spaces.

Aryeh Kontorovich, Sivan Sabato, Ruth Urner

J. Mach. Learn. Res., 2017

Nearly optimal classification for semimetrics.

Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

J. Mach. Learn. Res., 2017

The Expected Missing Mass under an Entropy Constraint.

Daniel Berend, Aryeh Kontorovich, Gil Zagdanski

Entropy, 2017

Advanced Analytics for Connected Cars Cyber Security.

Matan Levi, Yair Allouche, Aryeh Kontorovich

CoRR, 2017

Mixing time estimation in reversible Markov chains from a single sample path.

Daniel J. Hsu, Aryeh Kontorovich, David A. Levin, Yuval Peres, Csaba Szepesvári

CoRR, 2017

Boosting conditional probability estimators.

Dan Gutfreund, Aryeh Kontorovich, Ran Levy, Michal Rosen-Zvi

Ann. Math. Artif. Intell., 2017

Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions.

Aryeh Kontorovich, Sivan Sabato, Roi Weiss

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Adaptive metric dimensionality reduction.

Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer

Theor. Comput. Sci., 2016

The state complexity of random DFAs.

Daniel Berend, Aryeh Kontorovich

Theor. Comput. Sci., 2016

Concentration of measure without independence: a unified approach via the martingale method.

Aryeh Kontorovich, Maxim Raginsky

CoRR, 2016

Exact Lower Bounds for the Agnostic Probably-Approximately-Correct (PAC) Machine Learning Model.

Aryeh Kontorovich, Iosif Pinelis

CoRR, 2016

2015

A finite sample analysis of the Naive Bayes classifier.

Daniel Berend, Aryeh Kontorovich

J. Mach. Learn. Res., 2015

Local-shapelets for fast classification of spectrographic measurements.

Daniel Gordon, Danny Hendler, Aryeh Kontorovich, Lior Rokach

Expert Syst. Appl., 2015

Nearly optimal classification for semimetrics.

Lee-Ad Gottlieb, Aryeh Kontorovich

CoRR, 2015

Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path.

Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Bayes consistent 1-NN classifier.

Aryeh Kontorovich, Roi Weiss

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

On the Additive Properties of the Fat-Shattering Dimension.

Ohad Asor, Hubert Haoyang Duan, Aryeh Kontorovich

IEEE Trans. Neural Networks Learn. Syst., 2014

Efficient Classification for Metric Data.

Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer

IEEE Trans. Inf. Theory, 2014

Minimum KL-Divergence on Complements of $L_{1}$ Balls.

Daniel Berend, Peter Harremoës, Aryeh Kontorovich

IEEE Trans. Inf. Theory, 2014

Deciding unique decodability of bigram counts via finite automata.

Aryeh Kontorovich, Ari Trachtenberg

J. Comput. Syst. Sci., 2014

Uniform Chernoff and Dvoretzky-Kiefer-Wolfowitz-Type Inequalities for Markov Chains and Related Processes.

Aryeh Kontorovich, Roi Weiss

J. Appl. Probab., 2014

Near-optimal sample compression for nearest neighbors.

Lee-Ad Gottlieb, Aryeh Kontorovich

CoRR, 2014

Consistency of weighted majority votes.

Daniel Berend, Aryeh Kontorovich

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Maximum Margin Multiclass Nearest Neighbors.

Aryeh Kontorovich, Roi Weiss

Proceedings of the 31th International Conference on Machine Learning, 2014

Concentration in unbounded metric spaces and algorithmic stability.

Aryeh Kontorovich

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Exploiting label dependencies for improved sample complexity.

Lena Chekina, Dan Gutfreund, Aryeh Kontorovich, Lior Rokach, Bracha Shapira

Mach. Learn., 2013

On the learnability of shuffle ideals.

Dana Angluin, James Aspnes, Sarah Eisenstat, Aryeh Kontorovich

J. Mach. Learn. Res., 2013

Predictive PAC Learning and Process Decompositions.

Cosma Rohilla Shalizi, Aryeh Kontorovich

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Determining the unique decodability of a string in linear time.

Jiaxi Jin, Aryeh Kontorovich, Ari Trachtenberg

Proceedings of the 2013 Information Theory and Applications Workshop, 2013

Efficient determination of the unique decodability of a string.

Arnold Filtser, Jiaxi Jin, Aryeh Kontorovich, Ari Trachtenberg

Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

On learning parametric-output HMMs.

Aryeh Kontorovich, Boaz Nadler, Roi Weiss

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Statistical estimation with bounded memory.

Leonid Kontorovich

Stat. Comput., 2012

VC bounds on the cardinality of nearly orthogonal function classes.

Lee-Ad Gottlieb, Aryeh Kontorovich, Elchanan Mossel

Discret. Math., 2012

A Reverse Pinsker Inequality

Daniel Berend, Aryeh Kontorovich

CoRR, 2012

Efficiently decoding strings from their shingles

Aryeh Kontorovich, Ari Trachtenberg

CoRR, 2012

String reconciliation with unknown edit distance.

Aryeh Kontorovich, Ari Trachtenberg

Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

On the Learnability of Shuffle Ideals.

Dana Angluin, James Aspnes, Aryeh Kontorovich

Proceedings of the Algorithmic Learning Theory - 23rd International Conference, 2012

2011

Model Selection for Sinusoids in Noise: Statistical Analysis and a New Penalty Term.

Boaz Nadler, Leonid Kontorovich

IEEE Trans. Signal Process., 2011

Unique decodability of bigram counts by finite automata

Aryeh Kontorovich, Ari Trachtenberg

CoRR, 2011

Metric Anomaly Detection via Asymmetric Risk Minimization.

Aryeh Kontorovich, Danny Hendler, Eitan Menahem

Proceedings of the Similarity-Based Pattern Recognition - First International Workshop, 2011

2010

Lower Bounds on Learning Random Structures with Statistical Queries.

Dana Angluin, David Eisenstat, Leonid Kontorovich, Lev Reyzin

Proceedings of the Algorithmic Learning Theory, 21st International Conference, 2010

2009

Universal Kernel-Based Learning with Applications to Regular Languages.

Leonid Kontorovich, Boaz Nadler

J. Mach. Learn. Res., 2009

2008

Kernel methods for learning languages.

Leonid Kontorovich, Corinna Cortes, Mehryar Mohri

Theor. Comput. Sci., 2008

2007

A Universal Kernel for Learning Regular Languages.

Leonid Kontorovich

Proceedings of the Mining and Learning with Graphs, 2007

Learning Languages with Rational Kernels.

Corinna Cortes, Leonid Kontorovich, Mehryar Mohri

Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

2006

Learning Linearly Separable Languages.

Leonid Kontorovich, Corinna Cortes, Mehryar Mohri

Proceedings of the Algorithmic Learning Theory, 17th International Conference, 2006

2004

Uniquely decodable n-gram embeddings.

Leonid Kontorovich

Theor. Comput. Sci., 2004